Hamel’s Blog - Optimizing latency

The article provides a comprehensive comparison of various tools for optimizing latency in open source Language Learning Models (LLMs). The author finds that mlc is the fastest, CTranslate2 is the easiest to use with excellent documentation, and vLLM is the best for serving large models due to its support for distributed inference. However, Text Generation Inference (TGI) is only an okay option due to its slower speed and restrictive license. The author also provides a detailed benchmark table comparing the average time, token count, and other metrics for each tool.

The author further provides a background on the two categories of tools for model inference: Inference servers and Model Optimization. He also provides detailed notes on how to use each tool, including mlc, CTranslate2, TGI, Text Generation WebUI, vLLM, and the HuggingFace Inference Endpoint. The author concludes by noting that while some tools have both an inference server and optimization tools, it's common to use both categories in conjunction for efficient model serving.

Key takeaways:

The study found that mlc is the fastest tool for optimizing latency for open source LLMs, but the author expressed skepticism about its speed and plans to measure its quality.
CTranslate2 was identified as the author's favorite tool due to its speed, ease of use, and excellent documentation.
vLLM was noted for its speed and its support for distributed inference, making it potentially ideal for serving very large models.
Text Generation Inference was deemed an okay option, but not as fast as vLLM. Its recent license change to a more restrictive one may interfere with certain commercial uses.

Hamel’s Blog - Optimizing latency

Key takeaways:

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